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SentimentSound
Overview
This is a deep learning model for Speech Emotion Recognition that can classify audio clips into different emotional states. The model is trained on a dataset of speech samples and can identify emotions such as neutral, calm, happy, sad, angry, fearful, disgust, and surprised.
Model Details
- Model Type: Hybrid Neural Network (CNN + LSTM)
- Input: Audio features extracted from 3-second wav files
- Output: Emotion classification
Supported Emotions
- Neutral
- Calm
- Happy
- Sad
- Angry
- Fearful
- Disgust
- Surprised
Installation
Clone the Repository
git clone https://github.com/Vishal-Padia/SentimentSound.git
Dependencies
pip install -r requirements.txt
Usage Example
python emotion_predictor.py
Model Performance
- Accuracy: 85%
- Evaluation Metrics: Confusion matrix below
Training Details
- Feature Extraction:
- MFCC
- Spectral Centroid
- Chroma Features
- Spectral Contrast
- Zero Crossing Rate
- Spectral Rolloff
- Augmentation: Random noise and scaling applied
- Training Techniques:
- Class weighted loss
- AdamW optimizer
- Learning rate scheduling
- Gradient clipping
Limitations
- Works best with clear speech recordings
- Optimized for 3-second audio clips
- Performance may vary with different audio sources
Acknowledgments
- Dataset used for training (https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio)
Inference Providers
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